The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
As the population in cities increase, the number of competing businesses that perform the same function increase as well. With an increasing number of business options available to the average consumer, it becomes increasingly difficult for a consumer to determine which business to utilize for a specific service. For example, a person may live in an area with six different car repair shops. The person may run into difficulty determining which car repair shop will give the best service at the fairest rates.
A modern approach to the problem of identifying the best business to utilize is the creation of reviewing websites and applications. Reviewing websites and applications allow users to rate a business after the business has been utilized. The ratings for the business are aggregated into an overall review score. A reviewer may also leave comments about the business which are viewable by other users. Individuals viewing the review page for a business may see the number of people who have reviewed the business, the average review score received by the business, and comments about the business.
One issue with the modern approach is that a business may artificially increase its ratings by creating multiple accounts with the reviewing website and giving itself a good review on each account. Additionally, businesses may pay users with current accounts to give the business high ratings. Because the users of the reviewing accounts are not held accountable by the people reading the reviews, the users of the current accounts may be more willing to give a good review to a business without utilizing the business' services.
A second issue with the modern approach is that it aggregates reviews from multiple sources to which an individual reading the reviews has no connection. A recommendation from a friend or neighbor is inherently more trustworthy for most people than a recommendation from an unknown individual. Reviews from unknown individuals lack the weight of more personal recommendations from known or traceable individuals. Additionally, recommendations from unknown individuals lack the value of neighborhood based recommendations. Often, the favorite coffee shop for people who live in a neighborhood is different than the favorite coffee shop for tourists to the neighborhood.
Another issue with the modern approach is that it lacks the conversational aspect of a recommendation, which strengthens the value of the recommendation. If a person receives a recommendation from a friend for an automobile mechanic, the person is able to ask follow up questions to ensure that the recommended automobile mechanic matches the needs of the person. If a person posts on a social network a request for a recommendation of a good automobile mechanic, the person may receive multiple responses with multiple recommendations. From there, a conversation may continue which includes benefits and detriments of each automobile mechanic based on the experiences of other users. Based on this information, the person may make an informed decision. A friend of the person who also needs an automobile mechanic may view the conversation thread and make his own informed decision based on the conversation. In contrast, reviewing websites tend to be limited to including a single review from each individual which is focused on only that business. It would be difficult for a person to get the same conversational value from a reviewing website without personally contacting the multiple reviewers.
While social network conversations are useful for creating recommendations, they fail to preserve the recommendations for future viewers. For example, in May a recommendation conversation may occur regarding car mechanics. In July, the recommendation conversation for car mechanics would be buried behind other more recent conversations. Thus, a person searching for a car mechanic would either need to undertake the arduous task of looking through all past conversations for the specific discussion, or create a new discussion which may receive less responses.
Therefore, there is a need for a system that incorporates the trustworthiness of social connections with the conversational aspect that adds value to recommendations in order to create easily accessible and trustworthy recommendations for business entities.